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Utilizing hydrophone arrays for detecting underwater acoustic communication (UWAC) signals leverages spatial information to enhance detection efficiency and expand the perceptual range. This study redefines the task of UWAC signal detection as an object detection problem within the frequency–azimuth (FRAZ) spectrum. Employing Faster R-CNN as a signal detector, the proposed method facilitates the joint prediction of UWAC signals, including estimates of the number of sources, modulation type, frequency band, and direction of arrival (DOA). The proposed method extracts precise frequency and DOA features of the signals without requiring prior knowledge of the number of signals or frequency bands. Instead, it extracts these features jointly during training and applies them to perform joint predictions during testing. Numerical studies demonstrate that the proposed method consistently outperforms existing techniques across all signal-to-noise ratios (SNRs), particularly excelling in low SNRs. It achieves a detection F1 score of 0.96 at an SNR of −15 dB. We further verified its performance under varying modulation types, numbers of sources, grating lobe interference, strong signal interference, and array structure parameters. Furthermore, the practicality and robustness of our approach were evaluated in lake-based UWAC experiments, and the model trained solely on simulated signals performed competitively in the trials. [ABSTRACT FROM AUTHOR]

ObjectivesMethodsResultsDiscussionThe study aimed to explore whether TP could improve memory in the aged type 2 diabetic rat model by regulating microbiota-immune-synaptic plasticity axis.The experiment was divided into two parts. Firstly, to investigate the effects of TP on the physiopathology of the aged T2DM model rats, rats were randomly divided into the Normal control group, the aged group, the Aged T2DM model group, the TP 75, 150, 300 mg/kg groups, the 150 mg/kg Piracetam group and the 3 mg/kg Rosiglitazone group. Then, to further verify whether TP improved memory in aged T2DM rat model by regulating intestinal flora, the fecal microbiota transplantation (FMT) from the rats in the 300 mg/kg TP group into the rats in the aged T2DM model group was carried out. Effects on gut microbiota, colonic integrity (epithelial tight junction proteins), and endotoxemia (serum LPS) were examined, along with synaptic structure, synaptic plasticity-related structural proteins and inflammation signaling of the hippocampus in our study.Our results demonstrated that TP alleviated memory impairments in the aged T2DM rat model. The specific outcomes were as follows: TP 300 mg/kg corrected the gut dysbacteriosis, alleviated intestinal permeability reduction and peripheral/central inflammation, inhibited the TLR4/NF-κB signaling pathway. Meanwhile, TP improved the synaptic plasticity in the hippocampus of the aged T2DM model rats, whose expressions of SYN, PSD 95, NMDAR1 and GluR1 in hippocampus were significantly up-regulated. Surprisingly, rats of the FMT group displayed the same changes.TP improves the memory in aged T2DM rat model. The mechanism may be related to the alteration of gut flora, which can inhibit hippocampal TLR4/NF-κB signaling to attenuate neuroinflammation, then improve synaptic plasticity. The study proposes that TP interventions aimed at manipulating the gut microbiota may hold great potential as an effective approach for preventing and treating this disease. [ABSTRACT FROM AUTHOR]

"The EU Artificial Intelligence Act: Regulating Subliminal AI Systems" by Rostam J. Neuwirth explores the need for regulation in the field of Artificial Intelligence (AI) to address legal and ethical concerns. The book focuses on the specific challenge of regulating subliminal techniques used by AI systems. It examines the proposed EU Artificial Intelligence Act (AIA) and its objective, scope, and context. The book also delves into the psychology of subliminal messages and the potential negative effects they can have. It concludes by emphasizing the importance of global collaboration and a binding legal framework for regulating AI systems. This book provides valuable insights for researchers, policymakers, and legal professionals interested in understanding and addressing the impact of AI on society. [Extracted from the article]

In the ore beneficiation process, large block detection is necessary to ensure production safety. This typically involves identifying oversized ore on the conveyor belt and preventing material blockage accidents in the transfer buffer bin between the ore feeding belt and the ore receiving belt. Methods based on deep learning can learn to construct complex features from a large amount of data, but they also require a large number of hand-made datasets for training. Although the existing few shot detection methods for ore images reduce the cost of manual labeling, the corresponding detection performance is insufficient. This article mainly explores how to improve the performance of the detector under the ore image detection task in the case of few labeled images. First, a shot enhancement block is proposed to enhance the valuable foreground information for higher-quality support features. Subsequently, we present a dual-attention region proposal network that effectively leverages support features to enhance the precision of generating candidate proposals. Finally, we propose a lightweight multi-relational detector to effectively evaluate the relationship between query and support proposals, leading to a substantial enhancement in guidance performance. The proposed few-shot object detector (FS-OreDet) achieves the best detection results with state-of-the-art methods with an average precision (A P) of 55.1, a speed of 57 frames per second (F P S), and a model size of only 17 M B. Furthermore, our framework adeptly captures the feature information of ore images with substantial data. The detector's accuracy achieves a significant improvement of 14% in A P. Compared with general object detectors, the performance of the detector ranks first and meets the requirements for outdoor scene deployment. • Proposing a stronger few-shot object detector for ore images via feature enhancement and relationship exploration. • Some components are designed for boosting few-shot object detectors of ore images. • Extensive experiments on the ore dataset and theoretical analyses are also added to verify the superiority of the proposed detectors. [ABSTRACT FROM AUTHOR]

Squalene is a polyunsaturated triterpenoid compound present in various organisms. Squalene has multiple biological activities, which makes it a potential new functional food ingredient for nutrition enhancement or disease prevention. However, low extraction efficiency, high cost, and environmental issues greatly limit the practical application of squalene. This review discusses the latest findings on the biological activities, natural and genetically modified organism (GMO) sources, trends of extraction technologies, and delivery systems of squalene, providing innovative prospects for the large-scale production and application of squalene. Key findings and conclusions: Squalene has various biological activities, such as immunity enhancement, skin senility resistance, and hypolipidemic, antioxidant, antitumor, antibacterial, and detoxification effects. Thus, squalene can be widely applied in the food and pharmaceutical industries. Natural and GMO sources provide the foundation for large-scale squalene production. Innovative, green, sustainable extraction technologies are emerging as substitutes for traditional technologies. In addition, delivery systems can be used to maintain oxidation stability and efficacy of squalene in food applications. • Biological activities of squalene are presented. • Natural and GMO sources of squalene are summarized. • The advantages and limitations of squalene extraction technologies are discussed. • Delivery systems are beneficial for the application of squalene in food and drug. [ABSTRACT FROM AUTHOR]

The rising global population and improved living standards have led to an alarming increase in non‐communicable diseases, notably cardiovascular and chronic respiratory diseases, posing a severe threat to human health. Wearable sensing devices, utilizing micro‐sensing technology for real‐time monitoring, have emerged as promising tools for disease prevention. Among various sensing platforms, graphene‐based sensors have shown exceptional performance in the field of micro‐sensing. Laser‐induced graphene (LIG) technology, a cost‐effective and facile method for graphene preparation, has gained particular attention. By converting polymer films directly into patterned graphene materials at ambient temperature and pressure, LIG offers a convenient and environmentally friendly alternative to traditional methods, opening up innovative possibilities for electronic device fabrication. Integrating LIG‐based sensors into health monitoring systems holds the potential to revolutionize health management. To commemorate the tenth anniversary of the discovery of LIG, this work provides a comprehensive overview of LIG's evolution and the progress of LIG‐based sensors. Delving into the diverse sensing mechanisms of LIG‐based sensors, recent research advances in the domain of health monitoring are explored. Furthermore, the opportunities and challenges associated with LIG‐based sensors in health monitoring are briefly discussed. [ABSTRACT FROM AUTHOR]

Increasing evidence demonstrated that mitophagy and endoplasmic reticulum stress (ERS) was closely associated with memory decline in elderly type 2 diabetes mellitus (T2DM). Tea polyphenols (TP), an excellent natural antioxidant, has been reported to have neuroprotective properties in aging and diabetes, but the underlying mechanisms are still not fully understood. This study targets ERS-mitophagy in hippocampal neurons to investigate the improvement effect of memory in aged T2DM rats by TP. Rats were randomly divided into the control group, the aged group, the aged T2DM model group, the TP 75, 150, 300 mg/kg groups. TP 300 mg/kg ameliorated mitophagy by decreasing the levels of p-mTOR (S2448), P62 and HSP60 and increasing the levels of PINK1 and Parkin, the ratio of LC3Ⅱ/LC3Ⅰ, co-localization of LC3 and HSP60 and the number of autophagosomes and autolysosomes. TP 300 mg/kg attenuated ERS by downregulating the levels of p-PERK, p-eIF2α, ATF4, GRP78 and restoring the ER structure. To further verify epigallocatechin gallate (EGCG), which is the main active component of TP, enhanced mitophagy by inhibiting ERS, PC12 cells were pretreated with ERS activator tunicamycin (TM) or ERS inhibitor 4-phenylbutyric acid (4-PBA). The results showed that the improvement of mitophagy by EGCG was inhibited by TM and promoted by 4-PBA. Collectively, ERS-mitophagy in hippocampal neurons plays a key role in the improvement of memory by TP in aged T2DM rats. This study will provide a new perspective and strategy for the prevention of memory decline in elderly with T2DM. [Display omitted] • Tea polyphenols improves spatial memory and apoptosis in hippocampal neurons. • Mitophagy and endoplasmic reticulum stress involve in the apoptosis. • Endoplasmic reticulum stress mediated by PERK/eIF2α/ATF4 signaling pathway. • Mitophagy mediated by mTOR/PINK1/Parkin signaling pathway. • Tea polyphenols improves mitophagy by inhibiting endoplasmic reticulum stress. [ABSTRACT FROM AUTHOR]

Alzheimer’s disease (AD) is a progressive and irreversible neurological condition that occurs with age and poses a significant global public health concern, is distinguished by the degeneration of neurons and synapses in various regions of the brain. While the exact processes behind the neurodegeneration in AD are not completely known, it is now acknowledged that inflammation may have a significant impact on the beginning and advancement of AD neurodegeneration. The severity of many neurological illnesses can be influenced by the equilibrium between pro-inflammatory and anti-inflammatory mediators. The IL-1 family of cytokines is linked to innate immune responses, which are present in both acute inflammation and chronic inflammatory diseases. Research on the role of the IL-1 family in chronic neurological disease has been concentrated on AD. In this context, there is indirect evidence suggesting its involvement in the development of the disease. This review aims to provide a summary of the contribution of every IL-1 family member in AD pathogenesis, current immunotherapies in AD disease, and present treatment possibilities for either targeting or boosting these cytokines. [ABSTRACT FROM AUTHOR]

Image segmentation methods have been utilized to determine the particle size distribution of crushed ores. Due to the complex working environment, high-powered computing equipment is difficult to deploy. At the same time, the ore distribution is stacked, and it is difficult to identify the complete features. To address this issue, an effective box-supervised technique with texture features is provided for ore image segmentation that can identify complete and independent ores. Firstly, a ghost feature pyramid network (Ghost-FPN) is proposed to process the features obtained from the backbone to reduce redundant semantic information and computation generated by complex networks. Then, an optimized detection head is proposed to obtain the feature to maintain accuracy. Finally, Lab color space (Lab) and local binary patterns (LBP) texture features are combined to form a fusion feature similarity-based loss function to improve accuracy while incurring no loss. Experiments on MS COCO have shown that the proposed fusion features are also worth studying on other types of datasets. Extensive experimental results demonstrate the effectiveness of the proposed method, which achieves over 50 frames per second with a small model size of 21.6 MB. Meanwhile, the method maintains a high level of accuracy 67.8 in A P 50 b o x and 47.7 in A P 50 m a s k compared with the state-of-the-art approaches on ore image dataset, even better than bounding box tightness prior (BBTP) by 10.4/1.3 on A P 50 b o x / A P 50 m a s k metrics with the ResNet50 as backbone. The source code is available at https://github.com/MVME-HBUT/OREINST. • A fusion feature combining Lab color space (Lab) and local binary pattern (LBP) texture features is proposed. • A Ghost-FPN and an optimized detection head are presented to reduce model size while increasing inference speed. • Experiments on MS COCO have shown that the proposed fusion features are also worth studying on other types of datasets. [ABSTRACT FROM AUTHOR]